Intelligent SOX Estimation for Automotive Battery Management Systems: State-of-the-Art Deep Learning Approaches, Open Issues, and Future Research Opportunities

Lipu, Molla Shahadat Hossain; Karim, Tahia Fahrin; Ansari, Shaheer; Miah, Md. Sazal; Rahman, Md. Siddikur; Meraj, Sheikh Tanzim; Elavarasan, Rajvikram Madurai; Vijayaraghavan, Raghavendra Rajan

doi:10.3390/en16010023

Cited by 16 publications

(11 citation statements)

References 100 publications

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“…The main difference between FOM and ECM is that there are two CPE, so the discretization expressions of U 1 and U 2 need to be deduced. Combining Formula (4) and the G-L definition, the expression of U 1 can be obtained, as shown in Equation (11).…”

Section: Parameter Identification Strategy For Ga-psomentioning

confidence: 99%

“…ECM includes the Thevenin model, the partnership for a new generation of vehicles (PNGV) model, and the second‐order RC model. The Thevenin model has a simple structure and high accuracy and is widely used in practice 11 . The partnership for a new generation of vehicles (PNGV) model has higher estimation accuracy, but parameter identification is more complex 12 .…”

Section: Introductionmentioning

confidence: 99%

“…The Thevenin model has a simple structure and high accuracy and is widely used in practice. 11 The partnership for a new generation of vehicles (PNGV) model has higher estimation accuracy, but parameter identification is more complex. 12 There are many algorithms for estimating SOC based on ECM.…”

Section: Introductionmentioning

confidence: 99%

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Improved fractional‐order hysteresis‐equivalent circuit modeling for the online adaptive high‐precision state of charge prediction of urban‐electric‐bus lithium‐ion batteries

Zeng,

Wang,

Cao

et al. 2023

Circuit Theory & Apps

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SummaryAccurate state of charge (SOC) estimation is based on a precise battery model and is the focus of the battery management system (BMS). First, based on the second‐order RC equivalent circuit model and Grunwald–Letnikov (G‐L) definition, the high‐precision fractional‐order hysteresis‐equivalent circuit model (FH‐ECM) is established considering the open‐circuit voltage hysteresis effect. Then, the global parameters of the battery model are identified using a particle swarm algorithm optimized by the genetic algorithm (GA‐PSO). Third, a fractional‐order adaptive unscented Kalman filter (FOAUKF) algorithm is derived to estimate the SOC of lithium‐ion batteries. Finally, the feasibility of the model and algorithm is verified under complex working conditions. Under the dynamic stress test (DST) condition, the accuracy of model terminal voltage has been improved by 37.83%, and the error of SOC estimation has been reduced by 11.28%. Under Beijing bus dynamic stress test (BBDST) condition, the model terminal voltage accuracy has been improved by 51.44%, and the SOC estimation error has been reduced by 35.71%. The experimental results fully confirm the accuracy of the fractional‐order hysteresis‐equivalent circuit modeling method.

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Section: Parameter Identification Strategy For Ga-psomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved fractional‐order hysteresis‐equivalent circuit modeling for the online adaptive high‐precision state of charge prediction of urban‐electric‐bus lithium‐ion batteries

Zeng,

Wang,

Cao

et al. 2023

Circuit Theory & Apps

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“…Although it can improve estimation accuracy for certain data, it is difficult to adapt to multiple data conditions. 66 Comparison and analysis of SOC estimation methods.-In summary, many studies have been carried out on the SOC estimation of LIBs. Although the SOC estimation accuracy has been significantly improved, there are still many problems that have not been overcome.…”

Section: Soc Socmentioning

confidence: 99%

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

Wang¹,

Jia²,

Takyi‐Aninakwa³

et al. 2023

J. Electrochem. Soc.

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Lithium-ion batteries (LIBs) are used as energy storage systems due to their high efficiency. State of charge (SOC) estimation is one of the key functions of the battery management system (BMS). Accurate SOC estimation helps to determine the driving range and effective energy management of electric vehicles (EVs). However, due to complex electrochemical reactions and nonlinear battery characteristics, accurate SOC estimation is challenging. Therefore, this review examines the existing methods for estimating the SOC of LIBs and analyzes their respective advantages and disadvantages. Subsequently, a systematic and comprehensive analysis of the methods for constructing LIB models is conducted from various aspects such as applicability and accuracy. Finally, the advantages of particle filtering (PF) over the Kalman filter (KF) series algorithm for estimating SOC are summarized, and various improved PF algorithms for estimating the SOC of LIBs are compared and discussed. Additionally, this review provides corresponding suggestions for researchers in the battery field.

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“…There is great interest in describing, understanding, and modeling battery degradation. Numerous reviews in the literature discuss battery diagnosis, SOH prediction [128,129], SOX estimation [130,131], RUL estimation [132][133][134][135][136][137][138][139][140][141][142], battery charging, and fault prognostic methods. However, most of the models described in the literature are not chemical-agnostic and extrapolation from cell to pack level.…”

Section: Comparison With Other Studiesmentioning

confidence: 99%

A Comprehensive Review of EV Lithium-Ion Battery Degradation

Júnior¹,

Sanseverino²,

Gallo³

et al. 2023

Preprint

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Lithium-ion batteries with improved energy densities have made understanding the Solid Electrolyte Interphase (SEI) generation mechanisms that cause mechanical, thermal, and chemical failures more complicated. SEI processes reduce battery capacity and power. Thus, a review of this area's understanding is important. It is essential to know how batteries degrade in EVs to estimate battery lifespan as it goes, predict, and minimize losses, and determine the ideal time for a replacement. Lithium-ion batteries used in EVs mainly suffer two types of degradation: calendar degradation and cycling degradation. Despite the existence of several existing works in the literature, several aspects of battery degradation remain unclear or have not been analyzed in detail. This work presents a systematic review of existing works in the literature. The results of the present investigation provide insight into the complex relationships among various factors affecting battery degradation mechanisms. Specifically, this systematic review examined the effects of time, side reactions, temperature fluctuations, high charge/discharge rates, depth of discharge, mechanical stress, thermal stress, and the voltage relationship on battery performance and longevity. The results revealed that these factors interact in complex ways to influence the degradation mechanisms of batteries. For example, high charge currents and deep discharges were found to accelerate degradation, while low temperatures and moderate discharge depths were shown to be beneficial for battery longevity. Additionally, the results showed that the relationship between cell voltage and State-of-Charge (SOC) plays a critical role in determining the rate of degradation. Overall, these findings have important implications for the design and operation of battery systems, as they highlight the need to carefully manage a range of factors to maximize battery performance and longevity. The result is an analysis of the main articles published in this field in recent years. This work aims to present new knowledge about fault detection, diagnosis, and management of lithium-ion batteries based on battery degradation concepts. The new knowledge is presented and discussed in a structured and comprehensive way.

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Intelligent SOX Estimation for Automotive Battery Management Systems: State-of-the-Art Deep Learning Approaches, Open Issues, and Future Research Opportunities

Cited by 16 publications

References 100 publications

Improved fractional‐order hysteresis‐equivalent circuit modeling for the online adaptive high‐precision state of charge prediction of urban‐electric‐bus lithium‐ion batteries

Improved fractional‐order hysteresis‐equivalent circuit modeling for the online adaptive high‐precision state of charge prediction of urban‐electric‐bus lithium‐ion batteries

Review—Optimized Particle Filtering Strategies for High-Accuracy State of Charge Estimation of LIBs

A Comprehensive Review of EV Lithium-Ion Battery Degradation

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